What do small and mid-market businesses, non-profits, and small public and mission-based organizations have in common? The three-hat syndrome.

If you’ve ever worked in an organization of under 1,000, you know it very well. The supervisor who is also the acquisition person, who is also the customer service lead, who also does records management. The luxury of owning one lane quickly diminishes as an organization shrinks in size.

And this is exactly where AI adoption can become an amplifier for growth. If done well.

The challenge: Resources, always resources. The resources for the technology, the resources for the training, and the resources in the time it takes for the workforce to become proficient.

The KeyBank Middle Market Sentiment Survey conducted in February highlights the desire and optimism for bigger productivity gains and improvements driven by technology in mid-market businesses.

But when you have a workforce already spread thin, how do you get there? You may not, and this concern is reflected in the leaders surveyed, who identified AI-human collaboration as a top concern, along with managing job security worries.

These organizations are in a bind. They need their workforce to adopt these tools to realize business gains, but how are they going to manage the learning curve for adoption while keeping the business moving? And how are they going to assuage the unease of the very foundation their success depends on?

The margin for error is thinner, which means every step has to be more deliberate. And the first step is to understand where the workforce is. Not where leaders think they are. Not even what employees say they are. But the real measures of behavior that can’t simply be observed; they can only be measured.

The plan must start with organizations identifying the problem and its root cause, then answer:

  • Am I confident I’m solving the right problem?
  • What do I know about my people to solve that problem?
  • What tool am I going to use to solve the problem?
  • Do I understand the workflows impacted by my solution?
  • Are my projected returns realistic and measurable?

Skip any of those steps, and your adoption becomes a fourth hat that raises the stakes, raises the burden, raises costs, and threatens your actual goal, which is increased productivity and efficiency.

If you imagined this in a layered diagram, problem identification would be at the bottom, but the next layer would be the workforce. So the question remains: do organizations know enough about their people to ensure the foundation is strong before they build and implement their AI strategy?